融合数据驱动与卡尔曼滤波，提升锂电池电量估算精度与平滑性。
A virtual sensor fusion approach for state of charge estimation of lithium-ion cells
- 基于数据学习电池的仿射参数可变模型，构建线性观测器银行。
- 用观测器特征训练机器学习模型，输出电量预测值供卡尔曼滤波使用。
- 自适应校准噪声协方差，适合高精度电池管理系统设计。
本文提出一种虚拟传感器融合方法，用于锂离子电池的荷电状态（SOC）估计。该方法结合了基于等效电路模型（ECM）的卡尔曼滤波（KFs）与机器学习技术。具体流程为：(i) 从数据中直接学习电池的仿射参数可变（APV）模型；(ii) 由APV模型导出一组线性观测器；(iii) 利用观测器提取的特征、输入与输出数据训练机器学习模型，以预测SOC。将虚拟传感器输出的SOC预测值与电池端电压一同输入扩展卡尔曼滤波器（EKF），实现双范式融合。同时提出一种数据驱动的EKF噪声协方差矩阵校准策略。实验结果表明，该方法在SOC估计精度与平滑性方面均优于传统方法。
原文摘要 · Abstract (English)
This paper addresses the estimation of the State Of Charge (SOC) of lithium-ion cells via the combination of two widely used paradigms: Kalman Filters (KFs) equipped with Equivalent Circuit Models (ECMs) and machine-learning approaches. In particular, a recent Virtual Sensor (VS) synthesis technique is considered, which operates as follows: (i) learn an Affine Parameter-Varying (APV) model of the cell directly from data, (ii) derive a bank of linear observers from the APV model, (iii) train a machine-learning technique from features extracted from the observers together with input and output data to predict the SOC. The SOC predictions returned by the VS are supplied to an Extended KF (EKF) as output measurements along with the cell terminal voltage, combining the two paradigms. A data-driven calibration strategy for the noise covariance matrices of the EKF is proposed. Experimental results show that the designed approach is beneficial w.r.t. SOC estimation accuracy and smoothness.
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